Reifying Research Logic: AI-Assisted Workflow Construction and Incremental Refinement for Quantitative Syntax
3.20T1 sourcearXiv cs.MA
Source record
Published by arXiv cs.MA (T1 source). The original is at https://arxiv.org/abs/2608.10662.
Pipeline notes
The summary and note below are generated by the signal pipeline — they are Beyond Desk’s reading, not quotations from the source.
SummaryQLWF is a visual workflow platform for quantitative syntax research. Natural-language descriptions are converted to executable workflows through an AI-assisted five-stage pipeline; the LLM is used only during construction, with a fixed node library handling execution. A 64-task QL-Bench shows 98.4% output plausibility, and incremental refinement uses roughly one-third of the tokens of full regeneration.
Why it mattersReleases a concrete visual workflow tool plus a 64-task benchmark for an academic research domain. Incremental-refinement result (~1/3 tokens) is a usable design signal for similar LLM-assisted pipeline tools.
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